ANALYSIS OF PUBLIC SENTIMENT TOWARDS VILLAGE OFFICIALS IN DISTRIBUTION OF SOCIAL ASSISTANCE TO THE COMMUNITY

Abstract

The analysis of public sentiment toward village officials in the distribution of social assistance is essential to evaluate the level of public satisfaction and the effectiveness of social programs. This study aims to analyze community sentiment using Natural Language Processing (NLP) and sentiment classification techniques with the Naive Bayes algorithm. The dataset consists of public comments collected from social media, forums, and online surveys. The results show that most public sentiment is negative, dominated by issues of injustice, delays, and lack of transparency in the distribution process. Meanwhile, some comments reflect positive and neutral sentiments, indicating satisfaction or opportunities for service improvement. The classification model achieved an accuracy rate of 84%, proving its effectiveness in sentiment-based policy evaluation. These findings are expected to help village officials improve service quality by increasing transparency and public trust.